Global Trends and Exposure Profiling of Early‐Onset Type 2 Diabetes: An Ecological Analysis Using Machine Learning

ABSTRACT Background Early‐onset Type 2 diabetes (EOT2D) is an increasing global health concern. We examined global trends in EOT2D incidence and identified robust population‐level exposure correlates. Methods Using the data from the GBD 2021, we analysed EOT2D incidence among individuals aged 15–39 years across 204 countries and territories from 1990 to 2021. Temporal trends were assessed using estimated annual percentage change. Candidate exposures were evaluated using Elastic Net, Random Forest and XGBoost with temporally separated training, validation and test periods. Country‐level bootstrap resampling, robust rank aggregation (RRA) and SHapley Additive exPlanations (SHAP) analyses assessed stability and cross‐model consensus. Selected exposures were further examined using year fixed‐effects and two‐way fixed‐effects (TWFE) models. Composite exposure indices were constructed to characterize geographic patterns of joint exposure. Results EOT2D incidence increased globally, with persistently high rates in Pacific Island countries and rapid increases in Egypt, Greenland and Afghanistan. XGBoost showed the best out‐of‐time predictive performance. High body mass index and diet low in whole grains were the only cross‐model consensus predictive exposures retained by RRA after multiple‐testing correction and consistently ranked highest across models and temporal periods. In the year fixed‐effects model, both exposures were positively associated with EOT2D incidence. After adjustment for country fixed effects, high body mass index remained significant. Composite exposure indices showed marked geographic heterogeneity but broadly similar patterns across weighting strategies. Conclusions EOT2D incidence is rising globally. Population adiposity showed the most consistent association across analytical approaches, while the association with EOT2D incidence of low whole‐grain intake was more context dependent.